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Results 1 - 10
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2,720
On Bayesian adaptation
- Acta Appl. Math
, 2003
"... Summary: We consider estimating a probability density p based on a random sample from this density by a Bayesian approach. The prior is constructed in two steps, by first constructing priors on a collection of models each expressing a qualitative prior guess on the true density, and next combining t ..."
Abstract
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Cited by 17 (8 self)
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Summary: We consider estimating a probability density p based on a random sample from this density by a Bayesian approach. The prior is constructed in two steps, by first constructing priors on a collection of models each expressing a qualitative prior guess on the true density, and next combining
Quest: a bayesian adaptive psychometric method
- PERCEPT PSYCHOPHYS
, 1983
"... An adaptive psychometric procedure that places each trial at the current most probable Baye& ian estimate of threshold is described. The procedure takes advantage of the common finding that the human psychometric function is invariant in form when expressed as a function of log intensity. The pr ..."
Abstract
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Cited by 321 (25 self)
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An adaptive psychometric procedure that places each trial at the current most probable Baye& ian estimate of threshold is described. The procedure takes advantage of the common finding that the human psychometric function is invariant in form when expressed as a function of log intensity
Bayesian adaptive exploration
- in Statistical Challenges in Astronomy, 2003
"... Abstract. I describe a framework for adaptive scientific exploration based on iterating an Observation–Inference–Design cycle that allows adjustment of hypotheses and observing protocols in response to the results of observation on-the-fly, as data are gathered. The framework uses a unified Bayesian ..."
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Cited by 22 (1 self)
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of an extrasolar planet, and locating a hidden one-dimensional object—show the approach can significantly improve observational efficiency in settings that have well-defined nonlinear models. I conclude with a list of open issues that must be addressed to make Bayesian adaptive exploration a practical and reliable
Bayesian Adaptation Revisited
- Proc. ISCA ITRW ASR2000
"... We report the results of some preliminary experiments with a new method of acoustic-phonetic modeling for large vocabulary applications that can be viewed as a far-reaching extension of Bayesian speaker adaptation. This method adapts all of the Gaussian mean vectors in a speaker-independent HMM for ..."
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Cited by 7 (4 self)
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We report the results of some preliminary experiments with a new method of acoustic-phonetic modeling for large vocabulary applications that can be viewed as a far-reaching extension of Bayesian speaker adaptation. This method adapts all of the Gaussian mean vectors in a speaker-independent HMM
Bayesian Adaptive Nearest Neighbor
"... Abstract: The k nearest neighbor classification (k-NN) is a very simple and popular method for classification. However, it suffers from a major drawback, it assumes constant local class posterior probability. It is also highly dependent on and sensitive to the choice of the number of neighbors k. In ..."
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Cited by 2 (0 self)
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. In addition, it severely lacks the desired probabilistic formulation. In this article, we propose a Bayesian adaptive nearest neighbor method (BANN) that can adaptively select the shape of the neighborhood and the number of neighbors k. The shape of the neighborhood is automatically selected according
Bayesian adaptive inference and adaptive training
- IEEE Transactions Speech and Audio Processing
, 2007
"... Abstract—Large-vocabulary speech recognition systems are often built using found data, such as broadcast news. In contrast to carefully collected data, found data normally contains multiple acoustic conditions, such as speaker or environmental noise. Adaptive training is a powerful approach to build ..."
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Cited by 9 (7 self)
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to build systems on such data. Here, transforms are used to represent the different acoustic conditions, and then a canonical model is trained given this set of transforms. This paper describes a Bayesian framework for adaptive training and inference. This framework addresses some limitations of standard
Bayesian adaptive alignment and inference
- Proceedings of Fifth International Conference on Intelligent Systems for Molecular Biology
, 1997
"... Sequence alignment without the specification of gap penalties or a scoring matrix is attained by using Bayesian inference and a recursive algorithm. This procedure’s recursive algorithm sums over all possible alignments on the forward step to obtain normalizing constants essential to Bayesian infere ..."
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Cited by 4 (1 self)
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Sequence alignment without the specification of gap penalties or a scoring matrix is attained by using Bayesian inference and a recursive algorithm. This procedure’s recursive algorithm sums over all possible alignments on the forward step to obtain normalizing constants essential to Bayesian
Bayesian Adaptive Management With Learning
, 2008
"... Learning and taking action are generally treated as separate choices in adaptive management and decision-making under uncertainty. When management actions produce information, this dichotomous choice is no longer optimal the value of information and immediate returns should be considered simultaneou ..."
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Learning and taking action are generally treated as separate choices in adaptive management and decision-making under uncertainty. When management actions produce information, this dichotomous choice is no longer optimal the value of information and immediate returns should be considered
Results 1 - 10
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